/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    NormalEstimator.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.estimators;

import weka.core.Capabilities.Capability;
import weka.core.Capabilities;
import weka.core.RevisionUtils;
import weka.core.Statistics;
import weka.core.Utils;

/**
 * Simple probability estimator that places a single normal distribution over
 * the observed values.
 * 
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 5540 $
 */
public class NormalEstimator extends Estimator implements IncrementalEstimator {

	/** for serialization */
	private static final long serialVersionUID = 93584379632315841L;

	/** The sum of the weights */
	private double m_SumOfWeights;

	/** The sum of the values seen */
	private double m_SumOfValues;

	/** The sum of the values squared */
	private double m_SumOfValuesSq;

	/** The current mean */
	private double m_Mean;

	/** The current standard deviation */
	private double m_StandardDev;

	/** The precision of numeric values ( = minimum std dev permitted) */
	private double m_Precision;

	/**
	 * Round a data value using the defined precision for this estimator
	 * 
	 * @param data
	 *            the value to round
	 * @return the rounded data value
	 */
	private double round(double data) {

		return Math.rint(data / m_Precision) * m_Precision;
	}

	// ===============
	// Public methods.
	// ===============

	/**
	 * Constructor that takes a precision argument.
	 * 
	 * @param precision
	 *            the precision to which numeric values are given. For example,
	 *            if the precision is stated to be 0.1, the values in the
	 *            interval (0.25,0.35] are all treated as 0.3.
	 */
	public NormalEstimator(double precision) {

		m_Precision = precision;

		// Allow at most 3 sd's within one interval
		m_StandardDev = m_Precision / (2 * 3);
	}

	/**
	 * Add a new data value to the current estimator.
	 * 
	 * @param data
	 *            the new data value
	 * @param weight
	 *            the weight assigned to the data value
	 */
	public void addValue(double data, double weight) {

		if (weight == 0) {
			return;
		}
		data = round(data);
		m_SumOfWeights += weight;
		m_SumOfValues += data * weight;
		m_SumOfValuesSq += data * data * weight;

		if (m_SumOfWeights > 0) {
			m_Mean = m_SumOfValues / m_SumOfWeights;
			double stdDev = Math.sqrt(Math.abs(m_SumOfValuesSq - m_Mean
					* m_SumOfValues)
					/ m_SumOfWeights);
			// If the stdDev ~= 0, we really have no idea of scale yet,
			// so stick with the default. Otherwise...
			if (stdDev > 1e-10) {
				m_StandardDev = Math.max(m_Precision / (2 * 3),
				// allow at most 3sd's within one interval
						stdDev);
			}
		}
	}

	/**
	 * Get a probability estimate for a value
	 * 
	 * @param data
	 *            the value to estimate the probability of
	 * @return the estimated probability of the supplied value
	 */
	public double getProbability(double data) {

		data = round(data);
		double zLower = (data - m_Mean - (m_Precision / 2)) / m_StandardDev;
		double zUpper = (data - m_Mean + (m_Precision / 2)) / m_StandardDev;

		double pLower = Statistics.normalProbability(zLower);
		double pUpper = Statistics.normalProbability(zUpper);
		return pUpper - pLower;
	}

	/**
	 * Display a representation of this estimator
	 */
	public String toString() {

		return "Normal Distribution. Mean = " + Utils.doubleToString(m_Mean, 4)
				+ " StandardDev = " + Utils.doubleToString(m_StandardDev, 4)
				+ " WeightSum = " + Utils.doubleToString(m_SumOfWeights, 4)
				+ " Precision = " + m_Precision + "\n";
	}

	/**
	 * Returns default capabilities of the classifier.
	 * 
	 * @return the capabilities of this classifier
	 */
	public Capabilities getCapabilities() {
		Capabilities result = super.getCapabilities();
		result.disableAll();
		// class
		if (!m_noClass) {
			result.enable(Capability.NOMINAL_CLASS);
			result.enable(Capability.MISSING_CLASS_VALUES);
		} else {
			result.enable(Capability.NO_CLASS);
		}

		// attributes
		result.enable(Capability.NUMERIC_ATTRIBUTES);
		return result;
	}

	/**
	 * Return the value of the mean of this normal estimator.
	 * 
	 * @return the mean
	 */
	public double getMean() {
		return m_Mean;
	}

	/**
	 * Return the value of the standard deviation of this normal estimator.
	 * 
	 * @return the standard deviation
	 */
	public double getStdDev() {
		return m_StandardDev;
	}

	/**
	 * Return the value of the precision of this normal estimator.
	 * 
	 * @return the precision
	 */
	public double getPrecision() {
		return m_Precision;
	}

	/**
	 * Return the sum of the weights for this normal estimator.
	 * 
	 * @return the sum of the weights
	 */
	public double getSumOfWeights() {
		return m_SumOfWeights;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 5540 $");
	}

	/**
	 * Main method for testing this class.
	 * 
	 * @param argv
	 *            should contain a sequence of numeric values
	 */
	public static void main(String[] argv) {

		try {

			if (argv.length == 0) {
				System.out.println("Please specify a set of instances.");
				return;
			}
			NormalEstimator newEst = new NormalEstimator(0.01);
			for (int i = 0; i < argv.length; i++) {
				double current = Double.valueOf(argv[i]).doubleValue();
				System.out.println(newEst);
				System.out.println("Prediction for " + current + " = "
						+ newEst.getProbability(current));
				newEst.addValue(current, 1);
			}
		} catch (Exception e) {
			System.out.println(e.getMessage());
		}
	}
}
